Available sentiment classifiers typically describe statements as either positive or negative. While helpful for consumer products or marketing initiatives, this sort of binary classification is limiting for other types of sentiments, particularly those related to social causes. Our research contribution is the creation of new orthogonal sentiment classifiers unique to social causes. This new classification helps capture a more nuanced sentiment along level of support (enthusiastic/passive) and the degree of enthusiasm (enthusiastic/passive) toward a cause. Twitter data is noisy and content specific, making it difficult for any topic-specific approach. However, our findings show that Enthusiastic and Supportive tweets were more densely present in tweets about social causes in Twitter. Our research takes a computational approach to address how social media data, with a better classification of sentiment analysis for social causes, can be maximized by individuals and agencies. With a more nuanced classifier, users within social networks more receptive to social causes can be more easily identified for collective action and advocacy.